Techniques for compressing multiple-channel images

ABSTRACT

Disclosed are techniques for pre-processing an image for compression, e.g., one that includes a plurality of pixels, where each pixel is composed of sub-pixels that include at least an alpha sub-pixel. First, the alpha sub-pixels are separated into a first data stream. Next, invertible transformations are applied to the remaining sub-pixels to produce transformed sub-pixels. Next, for each row of the pixels: (i) identifying a predictive function that yields a smallest prediction differential total for the row, (ii) providing an identifier of the predictive function to a second data stream, and (iii) converting the transformed sub-pixels of the pixels in the row into prediction differentials based on the predictive function. Additionally, the prediction differentials for each of the pixels are encoded into first and second bytes that are provided to third and fourth data streams, respectively. In turn, the various data streams are compressed into a compressed image.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 15/665,404, entitled “TECHNIQUES FOR COMPRESSINGMULTIPLE-CHANNEL IMAGES,” filed Jul. 31, 2017, and claims the benefit ofU.S. Provisional Application No. 62/514,873, entitled “TECHNIQUES FORCOMPRESSING MULTIPLE-CHANNEL IMAGES,” filed Jun. 4, 2017, the content ofwhich is incorporated herein by reference in its entirety for allpurposes.

FIELD OF INVENTION

The embodiments described herein set forth techniques for compressingmultiple-channel images (e.g., red, green, blue, and alpha (RGBA)images). In particular, the techniques involve pre-processing the images(i.e., prior to compression) in a manner that can enhance resultingcompression ratios when the images are compressed using losslesscompressors (e.g., Lempel-Ziv-Welch (LZW)-based compressors).

BACKGROUND

Image compression techniques involve exploiting aspects of an image toreduce its overall size while retaining information that can be used tore-establish the image to its original (lossless) or near-original(lossy) form. Different parameters can be provided to compressors toachieve performance characteristics that best-fit particularenvironments. For example, higher compression ratios can be used toincrease the amount of available storage space within computing devices(e.g., smart phones, tablets, wearables, etc.), but this typically comesat a cost of cycle-intensive compression procedures that consumecorrespondingly higher amounts of power and time. On the contrary,cycle-efficient compression techniques can reduce power consumption andlatency, but this typically comes at a cost of correspondingly lowercompression ratios and amounts of available storage space withincomputing devices.

Notably, new compression challenges are arising as computing devicecapabilities are enhanced over time. For example, computing devices canbe configured (e.g., at a time of manufacture) to store thousands ofimages that are frequently-accessed by users of the computing devices.For example, a collection of emoticons can be stored at a givencomputing device, where it can be desirable to enable the collection ofemoticons to be frequently-accessed with low computational overhead.Additionally, although average storage space availability is also beingincreased over time, it can still be desirable to reduce a size of thecollection to increase the average storage space availability.

SUMMARY OF INVENTION

Representative embodiments set forth herein disclose techniques forcompressing multiple-channel images (e.g., red, green, blue, and alpha(RGBA) images). In particular, the techniques involve pre-processing theimages (i.e., prior to compression) in a manner that can enhanceresulting compression ratios when the images are compressed usinglossless compressors (e.g., Lempel-Ziv-Welch (LZW)-based compressors).

One embodiment sets forth a method for pre-processing a multiple-channelimage for compression. According to some embodiments, the method can beimplemented at a computing device, and can include a first step ofreceiving the multiple-channel image, where (i) the multiple-channelimage comprises a plurality of pixels, and (ii) each pixel is composedof sub-pixels that include at least an alpha sub-pixel. A next step ofthe method can include separating the alpha sub-pixels into a first datastream. Next, the method includes applying invertible transformations tothe remaining sub-pixels to produce transformed sub-pixels.Subsequently, the method includes carrying out the following steps foreach row of the pixels: (i) identifying a predictive function (from acollection of predictive functions) that yields a most-desirableprediction differential total for the row of pixels, (ii) providing anidentifier of the predictive function to a second data stream, and (iii)converting the transformed sub-pixels of the pixels in the row intoprediction differentials based on the predictive function. Additionally,the method can include, for each of the pixels: (i) encoding theprediction differentials of the pixel into a first byte and a secondbyte, and (ii) providing the first and second bytes to third and fourthdata streams, respectively. Finally, the method can include compressingthe first, second, third, and fourth data streams to produce acompressed image.

According to some embodiments, the foregoing method can be expanded toinclude additional steps that are carried out subsequent to applying theabove-described invertible transformations. In particular, theadditional steps can include applying one or more of an invertiblespatial transformation, a quantization transformation, an invertiblecolor palette transformation, or an invertible symmetry transformationto the remaining sub-pixels of each pixel.

Other embodiments include a non-transitory computer readable storagemedium configured to store instructions that, when executed by aprocessor included in a computing device, cause the computing device tocarry out the various steps of any of the foregoing methods. Furtherembodiments include a computing device that is configured to carry outthe various steps of any of the foregoing methods.

Other aspects and advantages of the invention will become apparent fromthe following detailed description taken in conjunction with theaccompanying drawings that illustrate, by way of example, the principlesof the described embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be readily understood by the following detaileddescription in conjunction with the accompanying drawings, wherein likereference numerals designate like structural elements.

FIG. 1 illustrates an overview of a computing device that can beconfigured to perform the various techniques described herein, accordingto some embodiments.

FIGS. 2A-2E illustrate a sequence of conceptual diagrams forpre-processing a multiple-channel image for compression, according tosome embodiments.

FIG. 3 illustrates a method for pre-processing a multiple-channel imagefor compression, according to some embodiments.

FIG. 4 illustrates a detailed view of a computing device that can beused to implement the various techniques described herein, according tosome embodiments.

FIG. 5 illustrates an overview of a modified version of the computingdevice of FIG. 1 that implements an expanded approach for pre-processinga multiple-channel image for compression, according to some embodiments.

FIGS. 6A-6H illustrate a sequence of conceptual diagrams for theexpanded approach for pre-processing a multiple-channel image forcompression, according to some embodiments.

FIGS. 7A-7B illustrate a method for the expanded approach forpre-processing a multiple-channel image for compression, according tosome embodiments.

DETAILED DESCRIPTION

Representative applications of methods and apparatus according to thepresent application are described in this section. These examples arebeing provided solely to add context and aid in the understanding of thedescribed embodiments. It will thus be apparent to one skilled in theart that the described embodiments can be practiced without some or allof these specific details. In other instances, well-known process stepshave not been described in detail in order to avoid unnecessarilyobscuring the described embodiments. Other applications are possible,such that the following examples should not be taken as limiting.

In the following detailed description, references are made to theaccompanying drawings, which form a part of the description and in whichare shown, by way of illustration, specific embodiments in accordancewith the described embodiments. Although these embodiments are describedin sufficient detail to enable one skilled in the art to practice thedescribed embodiments, it is understood that these examples are notlimiting such that other embodiments can be used, and changes can bemade without departing from the spirit and scope of the describedembodiments.

Representative embodiments set forth herein disclose techniques forcompressing multiple-channel images (e.g., red, green, blue, and alpha(RGBA) images). In particular, the techniques involve pre-processing theimages (i.e., prior to compression) in a manner that can enhanceresulting compression ratios when the images are compressed usinglossless compressors (e.g., Lempel-Ziv-Welch (LZW)-based compressors).

One embodiment sets forth a method for pre-processing a multiple-channelimage for compression. According to some embodiments, the method can beimplemented at a computing device, and can include a first step ofreceiving the multiple-channel image, where the multiple-channel imagecomprises a plurality of pixels (e.g., arranged according to arow/column layout). According to some embodiments, each pixel iscomposed of sub-pixels that include at least an alpha sub-pixel. Forexample, each pixel can also include a red sub-pixel, a green sub-pixel,and a blue sub-pixel. A next step of the method can include separatingthe alpha sub-pixels into a first data stream (e.g., in a left to right(i.e., row-wise)/top down (i.e., column-wise) order across the pixels).Next, the method includes applying invertible transformations to theremaining sub-pixels (e.g., the red, green, and blue subpixels) toproduce transformed sub-pixels. Subsequently, the method includesestablishing a second data stream that will be used to store identifiersfor different predictive functions that yield the best results for eachrow of the pixels. In particular, a next step of the method can involvecarrying out the following steps for each row of the pixels: (i)identifying a predictive function that yields a most-desirable (e.g., asmallest) prediction differential total for the row of pixels, (ii)providing an identifier of the predictive function to the second datastream, and (iii) converting the transformed sub-pixels of the pixels inthe row into prediction differentials based on the predictive function.Additionally, the method can involve establishing third and fourth datastreams that will be used to store different portions of the predictiondifferentials of the pixels. In particular, a next step of the methodcan include, for each of the pixels: (i) encoding the predictiondifferentials of the pixel into a first byte and a second byte, and (ii)providing the first and second bytes to the third and fourth datastreams, respectively. Finally, the method can include compressing thefirst, second, third, and fourth data streams to produce a compressedimage.

Accordingly, the techniques set forth herein involve pre-processing theimages (i.e., prior to compression) in a manner that can enhanceresulting compression ratios when the images are compressed usinglossless compressors (e.g., Lempel-Ziv-Welch (LZW)-based compressors). Amore detailed description of these techniques is provided below inconjunction with FIGS. 1, 2A-2E, and 3-4.

FIG. 1 illustrates an overview 100 of a computing device 102 that can beconfigured to perform the various techniques described herein. As shownin FIG. 1, the computing device 102 can include a processor 104, avolatile memory 106, and a non-volatile memory 124. It is noted that amore detailed breakdown of example hardware components that can beincluded in the computing device 102 is illustrated in FIG. 4, and thatthese components are omitted from the illustration of FIG. 1 merely forsimplification purposes. For example, the computing device 102 caninclude additional non-volatile memories (e.g., solid state drives, harddrives, etc.), other processors (e.g., a multi-core central processingunit (CPU)), and so on. According to some embodiments, an operatingsystem (OS) (not illustrated in FIG. 1) can be loaded into the volatilememory 106, where the OS can execute a variety of applications thatcollectively enable the various techniques described herein to beimplemented. For example, these applications can include an imageanalyzer 110 (and its internal components), one or more compressors 120,and so on.

According to some embodiments, the image analyzer 110 can be configuredto implement the techniques described herein that involve pre-processingmultiple-channel images 108 prior to compressing the multiple-channelimages 108. For example, a multiple-channel image 108 can represent ared, green, blue, and alpha (RGBA) image (e.g., an image of anemoticon). However, it is noted the techniques described herein can beapplied to any multiple-channel image 108 where compression enhancementscan be afforded. For example, the techniques can be applied tomultiple-channel images 108 having different resolutions, layouts,bit-lengths, and so on (compared to those described herein) withoutdeparting from the scope of this disclosure. It is noted that FIGS.2A-2E and 3 (and the corresponding descriptions set forth below) providea more detailed breakdown of the functionality of the image analyzer110, and that the following description of the image analyzer 110 withrespect to FIG. 1 is provided at a high level for simplificationpurposes.

As shown in FIG. 1, the multiple-channel image 108 is received by theimage analyzer 110. According to some embodiments, and as described ingreater detail herein, the multiple-channel image 108 can be composed ofa collection of pixels, where each pixel in the collection of pixelsincludes a group of sub-pixels (e.g., a red sub-pixel, a greensub-pixel, a blue sub-pixel, an alpha sub-pixel, etc.). It is noted thatthe term “sub-pixel” used herein can be synonymous with the term“channel.” Upon receipt of the multiple-channel image 108, an alphachannel separator 112 can be configured to (i) extract the alphasub-pixels from the collection of pixels, and (ii) store them into analpha channel data stream, which is described below in greater detail inconjunction with FIG. 2A.

Next, the remaining sub-pixels (e.g., the red, green, and bluesub-pixels) are provided to a color space transformer 114, a predictor116, and an encoder 118, to perform a series of operations prior tocompression. For example, the color space transformer 114 can beconfigured to apply an invertible transformation to the sub-pixels ofeach pixel to produce an equal number of transformed sub-pixel values,which is described below in greater detail in conjunction with FIG. 2B.Next, the predictor 116 can identify a predictive function (e.g., fromamong a group of available predictive functions) that yields the mostdesirable (e.g., the most accurate) results for each row of thetransformed sub-pixel values, which is described below in greater detailin conjunction with FIGS. 2C-2D. In particular, the predictor 116 canstore, into a predictive function data stream, an indication of therespective predictive functions identified/selected for the rows ofpixels. Additionally, the predictor 116 can apply the respectiveidentified predictive functions to the rows of the transformed sub-pixelvalues to produce prediction differentials (i.e., prediction errorsrelative to the value of the transformed sub-pixels). Subsequently, theencoder 118 can distribute the bits (i.e., binary values) of theprediction differentials into two corresponding bytes, which isdescribed below in greater detail in conjunction with FIG. 2E.Additionally, the encoder 118 can place the two corresponding bytes (foreach respective pixel) into buffer(s) 119. In turn, the compressor(s)120 can take action and compress the buffer(s) 119, which is alsodescribed below in greater detail in conjunction with FIG. 2E. Finally,the output(s) of the compressor(s) 120 can be joined together to producea compressed multiple-channel image 122, which is also described belowin greater detail in conjunction with FIG. 2E.

Notably, and according to some embodiments, the compressor(s) 120 can beconfigured to implement one or more compression techniques forcompressing the buffer(s) 119. For example, the compressors 120 canimplement Lempel-Ziv-Welch (LZW)-based compressors, other types ofcompressors, combinations of compressors, and so on. Moreover, thecompressor(s) 120 can be implemented in any manner to establish anenvironment that is most-efficient for compressing the buffer(s) 119.For example, multiple buffers 119 can be instantiated (where pixels canbe pre-processed in parallel), and each buffer 119 can be tied to arespective compressor 120 such that the buffers 119 can besimultaneously compressed in parallel as well. Moreover, the same or adifferent type of compressor 120 can be tied to each of the buffer(s)119 based on the formatting of the data that is placed into thebuffer(s) 119.

Accordingly, FIG. 1 provides a high-level overview of differenthardware/software architectures that can be implemented by computingdevice 102 in order to carry out the various techniques describedherein. A more detailed breakdown of these techniques will now beprovided below in conjunction with FIGS. 2A-2E and 3-4.

FIGS. 2A-2E illustrate a sequence of conceptual diagrams forpre-processing a multiple-channel image 108 for compression, accordingto some embodiments. In particular, the conceptual diagrams illustrate aseries of steps that the image analyzer 110 (and various sub-components)can be configured to carry out when pre-processing the multiple-channelimage 108 for compression by the compressor(s) 120. As shown in FIG. 2A,a first step 210 can involve the alpha channel separator 112 receivingthe multiple-channel image 108, which is composed of pixels 214 (denotedas “P”). As shown in FIG. 2A, the pixels 214 can be arranged accordingto a row/column layout, where the subscript of each pixel 214 “P” (e.g.,“1,1”) indicates the location of the pixel 214 in accordance with therows and columns. In the example illustrated in FIG. 2A, the pixels 214of the multiple-channel image 108 are arranged in an equal number ofrows and columns, such that the multiple-channel image 108 is a squareimage. However, it is noted that the techniques described herein can beapplied to multiple-channel images 108 having different layouts (e.g.,disproportionate row/column counts).

In any case, as shown in FIG. 2A, each pixel 214 is composed of foursub-pixels 216—a red sub-pixel 216 (denoted “R”), a green sub-pixel 216(denoted “G”), a blue sub-pixel (denoted “B”) 216, and an alphasub-pixel 216 (denoted “A”). It is noted that the alpha sub-pixel 216can be excluded from the sub-pixels 216 without departing from the scopeof this disclosure. In particular, the techniques performed inconjunction with step 210 of FIG. 2A can be omitted when themultiple-channel image 108 does not include an alpha channel (e.g., andinstead is an RGB image), while continuing to achieve at least a subsetof the compression benefits described herein. In any case, as shown inFIG. 2A, the alpha channel separator 112 can be configured to gather thealpha sub-pixels 216 and place them into an alpha channel data stream218. For example, the alpha channel separator 112 can extract the alphasub-pixels 216 in left to right (i.e., row-wise)/top down (i.e.,column-wise) order, and place the alpha sub-pixels 216 into the alphachannel data stream 218. For example, as shown in FIG. 2A, the alphasub-pixel 216 for the first pixel 214 “P(1,1)” can be extracted first,followed by the alpha sub-pixel 216 for the second pixel 214 “P(1,2)”,and so on. It is noted that the alpha sub-pixels 216 can be extracted inany order from the sub-pixels 216 (and placed into the alpha channeldata stream 218) without departing from the scope of this disclosure. Inany case, at a later time, the alpha channel data stream 218 will begrouped with additional data streams that are provided to thecompressor(s) 120, which is described below in greater detail inconjunction with FIGS. 2B-2E.

Next, FIG. 2B illustrates a step 220 in which the color spacetransformer 114 performs a collection of invertible transformationfunctions 222 on the remaining sub-pixels 216 (e.g., the red sub-pixels216, the green sub-pixels 216, and the blue sub-pixels 216) of thepixels 214 to produce transformed sub-pixels 224. For example, as shownin FIG. 2B, the transformation functions 222 can involve carrying out aseries of operations on the different sub-pixel values 216 to produce aluma value Y, a first chroma value CO, and a second chroma value CG. Inparticular, and according to some embodiments, the red sub-pixel 216 canbe replaced by the luma value Y (e.g., 8 bits), the green sub-pixel 216can be replaced by the first chroma value CO (e.g., 8 bits), and theblue sub-pixel 216 can be replaced by the second chroma value CG (e.g.,8 bits). As shown in FIG. 2B, the first chroma value CO and the secondchroma value CG can each potentially take the form of a negative number.Accordingly, the color space transformer 114 can add a sign bit to thefirst chroma value CO and the second chroma value CG to account for thepotential negative values (as illustrated by the event 223 in FIG. 2B),such that the first and second chroma values CO/CG are 9 bits in length.It is noted that the bit lengths described herein are merely exemplary,and that any bit-length can be utilized without departing from the scopeof this disclosure. In any case, at this juncture, the overallcharacteristics of the multiple-channel image 108 are transformed in amanner that enables subsequent functions to be applied to ultimatelyimprove compression ratios when compressing the multiple-channel image108, which is described below in greater detail.

Turning now to FIG. 2C, a step 230 can involve the predictor 116identifying, for each row of the transformed sub-pixels 224, apredictive function 236 (from among a group of predictive functions 236)that yields the most desirable prediction differentials 232 (i.e.,prediction error) within the scope of the row. For example, an examplescenario is shown FIG. 2C in which the second row of the transformedsub-pixels 224 that correspond to the luma value Y are processed usingeach of the Left, Up, Mean, and Custom predictive functions 236 toproduce prediction differentials 232. Although not illustrated in FIG.2C, it will be understood that the example scenario can also involveprocessing the second row of the different transformed sub-pixels 224that correspond to the chroma values CO/CG using each of the Left, Up,Mean, and Custom predictive functions 236 to produce predictiondifferentials 232. In turn, the prediction differentials 232(corresponding to the luma value Y and chroma values CO/CG within thesecond row) can be summed to establish a prediction differential total(performed separately for each of the predictive functions 236 appliedagainst the second row). For example, as illustrated in FIG. 2C, thecolumn “Differential Total” in the table of predictive functions 236 canbe populated, for each row of transformed sub-pixels 224, with thevalues of the differential totals for the different predictive functions236 applied against the row so that the most desirable predictivefunction 236 can be identified for the row.

In turn the predictor 116 can identify a most effective predictivefunction 236 for the second row based on the prediction differentialtotals. It is noted that the predictor 116 can implement any form ofarithmetic when calculating the prediction differentials 232/predictiondifferential totals described herein. For example, the predictor 116 canbe configured to sum the absolute value of the prediction differentials232 for the transformed sub-pixels 224 of a given row (for each of thedifferent predictive functions 236), and select the predictive function236 that yields the smallest prediction differential total. In anotherexample, the predictor 116 can (i) sum the prediction differentials 232for the transformed sub-pixels 224 of a given row (for each of thedifferent predictive functions 236), (ii) take the logarithm of the sumsto produce logarithmic values, and (iii) select the predictive function236 that yields the smallest logarithmic value. It is noted that thepredictive functions 236 illustrated in FIG. 2C are merely exemplary,and that the predictor 116 can be configured to implement anynumber/type of predictive functions 236 without departing from the scopeof this disclosure.

In any case, when the predictor 116 identifies a predictive function 236that is most appropriate for a given row of transformed sub-pixels 224,the predictor 116 can store an identifier for the predictive function236 within a predictive function data stream 242, as illustrated at step240 of FIG. 2D. For example, as shown in FIG. 2D, the predictor 116 canidentify the index of the selected predictive function 236 (e.g., inaccordance with the table of predictive functions 236 illustrated inFIG. 2C) for each row of the transformed sub-pixels 224, andsequentially store the indices of the selected predictive functions 236into the predictive function data stream 242. Thus, at the conclusion ofstep 240 in FIG. 2D, two separate data streams have been prepared forthe compressor(s) 120—the alpha channel data stream 218 and thepredictive function data stream 242. At this juncture, the selectedpredictive functions 236 are applied against their respective rows oftransformed sub-pixels 224 to establish the prediction differentials 232(e.g., as illustrated in FIG. 2C). Additionally, as shown in FIG. 2C,sign bits can be added (as illustrated by the element 226 in FIG. 2C) tothe prediction differentials 232 to account for any negativedifferential values established by way of the predictive functions 236(e.g., by subtracting predicted values from their respective transformedsub-pixels 224). At this juncture, additional operations can beperformed against the prediction differentials 232 to further-enhancethe resulting compression ratios that can be achieved, which aredescribed below in greater detail in conjunction with FIG. 2E.

As shown in FIG. 2E, a final step 250 can involve the encoder 118separating the prediction differentials 232—specifically, the bits ofeach of prediction differential 232—into a least significant byte 252and a most significant byte 254. For example, as shown in FIG. 2E, theprediction differential 232 for the luma value Y (of the pixel 214(1,1))—which includes (1) a sign bit (S16) established by way of thepredictive functions 236 (applied at step 230 of FIG. 2C), and (2) up tofifteen magnitude bits (M15-M1) (i.e., the prediction differential 232itself)—can be separated into a least significant byte 252-1 and a mostsignificant byte 254-1. In particular, the sign bit (S16) can bepositioned within a least significant bit of the least significant byte252, followed by seven of the least significant magnitude bits (M7-M1).Additionally, eight of the most significant magnitude bits (M15-M8) canbe positioned within the most significant byte 254-1.

Additionally, as shown in FIG. 2E, the prediction differential 232 forthe chroma value CO (of the pixel 214 (1,1))—which includes (1) a signbit (S16) established by way of the predictive functions 236 (applied atstep 230 of FIG. 2C), and (2) up to fifteen magnitude bits (M15-M1)(i.e., (i) the first sign bit established by the transformationfunctions 222 (applied at step 220 of FIG. 2B), and (ii) the predictiondifferential 232 itself)—can be separated into a least significant byte252-2 and a most significant byte 254-2. Similarly, the predictiondifferential 232 for the chroma value CG (of the pixel 214 (1,1))—whichincludes (1) the sign bit (S16) established by way of the predictivefunctions 236 (applied at step 230 of FIG. 2C), and (2) up to fifteenmagnitude bits (M15-M1) (i.e., (i) the first sign bit established by thetransformation functions 222 (applied at step 220 of FIG. 2B), and (ii)the prediction differential 232 itself)—can be separated into a leastsignificant byte 252-3 and a most significant byte 254-3.

It is noted that the encoder 118 can perform the foregoing techniquesusing a variety of approaches, e.g., performing in-place modificationsif the prediction differentials 232 (and ordered according to thedistribution illustrated in FIG. 2D), copying the predictiondifferentials 232 into respective data structures for the leastsignificant bytes 252 and the most significant bytes 254 (and orderedaccording to the distribution illustrated in FIG. 2D), and so on. It isalso noted that the distributions illustrated in FIG. 2E and describedherein are exemplary, and that any distribution of the bits of theprediction differentials 232 can be utilized without departing from thescope of this disclosure.

In any case, when the encoder 118 establishes the least significantbytes 252 and the most significant bytes 254 for each of the predictiondifferentials 232, the encoder 118 can group the least significant bytes252 into a least significant byte data stream 256 (e.g., in a left toright (i.e., row-wise)/top down (i.e., column-wise) order). Similarly,the encoder 118 can group the most significant bytes 254 into a mostsignificant bye data stream 258 (e.g., in a left to right (i.e.,row-wise)/top down (i.e., column-wise) order). At this juncture, fourdata streams have been established: the alpha channel data stream 218,the predictive function data stream 242, the least significant byte datastream 256, and the most significant byte data stream 258. In turn, theencoder 118 can provide these data streams into the buffer(s) 119, andinvoke the compressor(s) 120 to compress the buffer(s) 119.Subsequently, the compressor(s) 120 can take action and compress thecontents of the buffer(s) 119 to produce a compressed output. In thismanner, the compressed outputs can be joined together to produce acompressed multiple-channel image 122.

Additionally, it is noted that the image analyzer 110 can be configuredto pre-process the multiple-channel image 108 using other approaches toidentify additional optimizations that can be afforded with respect tocompressing the multiple-channel image 108. For example, the imageanalyzer 110 can be configured to take advantage of any symmetry that isidentified within the multiple-channel image 108. For example, the imageanalyzer 110 can be configured to (1) identify vertical symmetry,horizontal symmetry, diagonal symmetry, etc., within themultiple-channel image 108, (2) carve out the redundant pixels 214, and(3) process the remaining pixels 214. For example, when amultiple-channel image 108 is both vertically and horizontallysymmetric, the image analyzer 110 can process only a single quadrant ofthe multiple-channel image 108 to increase efficiency. In anotherexample, when the multiple-channel image 108 is diagonally symmetrical,the image analyzer 110 can process only a single triangular portion ofthe multiple-channel image 108 to increase efficiency. In any case, whenthese efficiency measures are invoked, the image analyzer 110 can beconfigured to store, within the compressed multiple-channel image 122,information about the symmetry so that the disregarded portions can bere-established when the compressed multiple-channel image 122 isdecompressed/rebuilt at the computing device 102.

FIG. 3 illustrates a method 300 for pre-processing a multiple-channelimage 108 for compression, according to some embodiments. As shown inFIG. 3, the method 300 begins at step 302, where the image analyzer 110receives image data for the multiple-channel image 108. As describedherein, the image data can be composed of a plurality of pixels 214,where each pixel 214 of the plurality of pixels 214 is composed ofsub-pixels 216 that include: a red sub-pixel 216, a green sub-pixel 216,a blue sub-pixel 216, and an alpha sub-pixel 216.

At step 304, the image analyzer 110 separates the alpha sub-pixels 216into a first data stream (e.g., the alpha channel data stream 218described above in conjunction with FIG. 2A). At step 306, the imageanalyzer 110 applies, for each pixel 214 of the plurality of pixels 214,invertible transformations (e.g., the transformation functions 222described above in conjunction with FIG. 2B) to the remaining sub-pixels216 of the pixel 214 to produce transformed sub-pixels (e.g., thetransformed sub-pixels 224 described above in conjunction with FIG. 2B).At step 308, the image analyzer 110 establishes a second data stream(e.g., the predictive functions data stream 242 described above inconjunction with FIG. 2D). At step 310, the image analyzer 110 performsthe following for each row of pixels 214 in the plurality of pixels 214:(i) identifying a predictive function (e.g., a predictive function 236,as described above in conjunction with FIG. 2C) that yields a mostdesirable prediction differential total for the row of pixels 214 (e.g.,as described above in conjunction with FIG. 2C), (ii) providing anidentifier of the predictive function 236 to the second data stream(e.g., as described above in conjunction with FIG. 2D), and (iii)converting the transformed sub-pixels 224 of the pixels in the row ofpixels 214 into prediction differentials 232 based on the predictivefunction 236 (e.g., as described above in conjunction with FIGS. 2C-2D).

At step 312, the image analyzer 110 establishes a third data stream anda fourth data stream (e.g., the least significant byte data stream 256and the most significant byte data stream 258, as described above inconjunction with FIG. 2E). At step 314, the image analyzer 110 performsthe following for each pixel 214 of the plurality of pixels 214: (i)encoding the prediction differentials 232 of the pixel 214 into a firstbyte and a second byte (e.g., as described above in conjunction withFIG. 2E), and (ii) providing the first byte and second bytes to thethird and fourth data streams, respectively (e.g., as described above inconjunction with FIG. 2E). Finally, at step 316, the image analyzer 110compresses the first, second, third, and fourth data streams (e.g., asdescribed above in conjunction with FIG. 2E) using the compressor(s)120.

FIG. 4 illustrates a detailed view of a computing device 400 that can beused to implement the various techniques described herein, according tosome embodiments. In particular, the detailed view illustrates variouscomponents that can be included in the computing device 102 described inconjunction with FIG. 1. As shown in FIG. 4, the computing device 400can include a processor 402 that represents a microprocessor orcontroller for controlling the overall operation of the computing device400. The computing device 400 can also include a user input device 408that allows a user of the computing device 400 to interact with thecomputing device 400. For example, the user input device 408 can take avariety of forms, such as a button, keypad, dial, touch screen, audioinput interface, visual/image capture input interface, input in the formof sensor data, and so on. Still further, the computing device 400 caninclude a display 410 that can be controlled by the processor 402 (e.g.,via a graphics component) to display information to the user. A data bus416 can facilitate data transfer between at least a storage device 440,the processor 402, and a controller 413. The controller 413 can be usedto interface with and control different equipment through an equipmentcontrol bus 414. The computing device 400 can also include a network/businterface 411 that couples to a data link 412. In the case of a wirelessconnection, the network/bus interface 411 can include a wirelesstransceiver.

As noted above, the computing device 400 also includes the storagedevice 440, which can comprise a single disk or a collection of disks(e.g., hard drives). In some embodiments, storage device 440 can includeflash memory, semiconductor (solid state) memory or the like. Thecomputing device 400 can also include a Random-Access Memory (RAM) 420and a Read-Only Memory (ROM) 422. The ROM 422 can store programs,utilities or processes to be executed in a non-volatile manner. The RAM420 can provide volatile data storage, and stores instructions relatedto the operation of applications executing on the computing device 400,e.g., the image analyzer 110/compressor(s) 120.

Additionally, FIG. 5 illustrates an overview 500 of a modified versionof the computing device 102 of FIG. 1 that implements an expandedapproach for pre-processing a multiple-channel image 108 forcompression, according to some embodiments. In particular, and as shownin FIG. 5, the image analyzer 110 illustrated in FIG. 5 can beconfigured to include the same components as the image analyzer 110illustrated in FIG. 1, with the addition of a spatial transformer 502, aquantizer 504, a palette identifier 506, and a symmetry identifier 508.As described in greater detail below in conjunction with FIGS. 6A-6H and7A-7B, these various components can further-improve the overallcompression ratios that are achieved when pre-processingmultiple-channel images 108.

FIGS. 6A-6H illustrate a sequence of conceptual diagrams for theexpanded approach for pre-processing a multiple-channel image 108 forcompression, according to some embodiments. In particular, theconceptual diagrams illustrate a series of steps that the image analyzer110 (and various sub-components) illustrated in FIG. 5 can be configuredto carry out when pre-processing the multiple-channel image 108 forcompression by the compressor(s) 120. As shown in FIG. 6A, a first step601 can involve the alpha channel separator 112 receiving themultiple-channel image 108, which is composed of pixels 614 (denoted as“P”). As shown in FIG. 6A, the pixels 614 can be arranged according to arow/column layout, where the subscript of each pixel 614 “P” (e.g.,“1,1”) indicates the location of the pixel 614 in accordance with therows and columns. In the example illustrated in FIG. 6A, the pixels 614of the multiple-channel image 108 are arranged in an equal number ofrows and columns, such that the multiple-channel image 108 is a squareimage. However, it is again noted that the techniques described hereincan be applied to multiple-channel images 108 having different layouts(e.g., disproportionate row/column counts).

In any case, as shown in FIG. 6A, each pixel 614 is composed of foursub-pixels 616—a red sub-pixel 616 (denoted “R”), a green sub-pixel 616(denoted “G”), a blue sub-pixel (denoted “B”) 616, and an alphasub-pixel 616 (denoted “A”). It is noted that the alpha sub-pixel 616can be excluded from the sub-pixels 616 without departing from the scopeof this disclosure. In particular, the techniques performed inconjunction with step 601 of FIG. 6A can be omitted when themultiple-channel image 108 does not include an alpha channel (e.g., andinstead is an RGB image), while continuing to achieve at least a portionof the compression benefits described herein. In any case, as shown inFIG. 6A, the alpha channel separator 112 can be configured to gather thealpha sub-pixels 616 and place them into an alpha channel data stream618. For example, the alpha channel separator 112 can extract the alphasub-pixels 616 in a left to right (i.e., row-wise)/top down (i.e.,column-wise) order, and place the alpha sub-pixels 616 into the alphachannel data stream 618. For example, as shown in FIG. 6A, the alphasub-pixel 616 for the first pixel 614 “P(1,1)” can be extracted first,followed by the alpha sub-pixel 616 for the second pixel 614 “P(1,2)”,and so on. It is noted that the alpha sub-pixels 616 can be extracted inany order from the sub-pixels 616 (and placed into the alpha channeldata stream 618) without departing from the scope of this disclosure. Inany case, at a later time, the alpha channel data stream 618 will begrouped with additional data streams that are provided to thecompressor(s) 120, which is described below in greater detail inconjunction with FIGS. 6G-6H.

Next, FIG. 6B illustrates a step 602 in which the color spacetransformer 114 performs a collection of invertible color spacetransformation functions 622 on the remaining sub-pixels 616 (e.g., thered sub-pixels 616, the green sub-pixels 616, and the blue sub-pixels616) of the pixels 614 to produce sub-pixels 624. For example, as shownin FIG. 6B, the color space transformation functions 622 can involvecarrying out a series of operations on the different sub-pixel values216 to produce a luma value Y, a first chroma value CO, and a secondchroma value CG. In particular, and according to some embodiments, thered sub-pixel 616 can be replaced by the luma value Y (e.g., 8 bits),the green sub-pixel 616 can be replaced by the first chroma value CO(e.g., 8 bits), and the blue sub-pixel 616 can be replaced by the secondchroma value CG (e.g., 8 bits). As shown in FIG. 6B, the first chromavalue CO and the second chroma value CG can each potentially take theform of a negative number. Accordingly, the color space transformer 114can add a sign bit to the first chroma value CO and the second chromavalue CG to account for the potential negative values (as illustrated bythe event 623 in FIG. 6B), such that the first and second chroma valuesCO/CG are 9 bits in length. It is noted that the bit lengths describedherein are merely exemplary, and that any bit-length can be utilizedwithout departing from the scope of this disclosure. In any case, atthis juncture, the overall characteristics of the multiple-channel image108 are transformed in a manner that enables subsequent functions to beapplied to ultimately improve compression ratios when compressing themultiple-channel image 108, which is described below in greater detail.

Turning now to FIG. 6C, a step 603 can involve the spatial transformer502 applying additional invertible spatial transformation functions 626to each of the sub-pixels 624. According to some embodiments, thespatial transformer 502 can be configured to apply the invertiblespatial transformation functions 626 to one or more of the luma value Y,the first chroma value CO, or the second chroma value CG for each of thesub-pixels 624. The invertible spatial transformation function 626applied to each sub-pixel 624 can represent, for example, a Haaraverage-difference transformation, as illustrated in FIG. 6C. It isnoted that the use of the Haar average-difference transformation isexemplary, and that the spatial transformer 502 can employ othertransformations that are distinct from the Haar average-differencetransformation without departing from the scope of this disclosure.

In any case, the Haar average-difference transformation illustrated inFIG. 6C can involve calculating—for (i) the luma value Y, (ii) the firstchroma value CO, and/or (iii) the second chroma value CG of eachsub-pixel 624 of the sub-pixels 624—a “difference” value and a “mean”value. For example, the difference value for a luma value Y of a firstsub-pixel 624 (e.g., Y_(1,1)) can be calculated using a luma value Y ofa second sub-pixel 624 (e.g., Y_(1,2)) that is nearby the firstsub-pixel 624 (e.g., side-by-side sub-pixels 624). Additionally, themean value for the luma value Y of the first sub-pixel 624 (i.e.,Y_(1,1)) can be calculated using the distance value divided by aninteger (e.g., two). As shown in FIG. 6C, the same approaches can beapplied to the first chroma value CO and the second chroma value CG foreach sub-pixel 624. However, it is noted that any subset of the lumavalue Y, the first chroma value CO, and the second chroma value CG canbe excluded when performing the invertible transformations illustratedin FIG. 6C without departing from the scope of this disclosure. In anycase, the sub-pixels 628 illustrated in FIG. 6C represent the resultingsub-pixels after the sub-pixels 624 are transformed by the spatialtransformer 502.

Turning now to FIG. 6D, a step 604 can involve the quantizer 504applying “lossy” quantization adjustments 630 against the differencevalues calculated for the sub-pixels 628 (as described above inconjunction with FIG. 6C). According to some embodiments, and as shownin FIG. 6D, the quantization adjustments 630 can involve—for one or moreof the luma value Y, the first chroma value CO, or the second chromavalue CG for each of the sub-pixels 624—dividing the respectivedifference value by an integer (e.g., two). For example, for a givensub-pixel 628, the difference value (i.e., “DiffY”) for the luma value Ycan be quantized by dividing the DiffY value by two. Similarly, for thesub-pixel 628, the difference value (i.e., “DiffCo”) for the firstchroma value Co can be quantized by dividing the DiffCo value by two.Further, for the sub-pixel 628, the difference value (i.e., “DiffCg”)for the second chroma value Cg can be quantized by dividing the DiffCgvalue by two. It is noted that any subset of the luma value Y, the firstchroma value CO, and the second chroma value CG can be excluded whenperforming the quantization adjustments illustrated in FIG. 6D withoutdeparting from the scope of this disclosure. In any case, the sub-pixels632 illustrated in FIG. 6D represent the resulting sub-pixels after thesub-pixels 628 are transformed by the quantizer 504.

Turning now to FIG. 6E, a step 605 can involve the palette identifier506 transforming the sub-pixels 632 into sub-pixels 634 based on apalette representation. In particular, and according to someembodiments, the palette identifier 506 can be configured to identifyall unique pairs of first and second chroma values (CO, CG) for each ofthe sub-pixels 632 to establish a palette. For example, the palette canbe represented in an array having a number of entries that matches theidentified unique pairs of first and second chroma values (CO, CG),where each entry stores the values for the respective pair of first andsecond chroma values (CO, CG). In this regard, the first and secondchroma values (CO, CG) for each of the sub-pixels 634 can be updated toreflect an index within the array that corresponds to the first andsecond chroma values (CO, GC), thereby compacting the overall amount ofdata that otherwise is required to store the first and second chromavalues (CO, CG). According to some embodiments, the palette identifier506 can analyze whether the palette representation (i.e., the sub-pixels634) contributes to a compression ratio that exceeds a compression ratiothat would otherwise be achieved if the palette transformation isdisregarded (i.e., the sub-pixels 632). According to some embodiments,when the number of identified unique pairs of first and second chromavalues (CO, CG) is less than or equal to two hundred fifty-six (256), aleast significant byte (LSB) stream of the indices is sufficient.Otherwise, a most significant byte (MSB) stream of the indices can beused as well. Notably, when the MSB stream of indices is utilized, theLSB and MSB streams can be separated from the corresponding luma valuesY.

Additionally, FIG. 6E can additionally include a step 606 that involvesthe symmetry identifier 508 transforming the sub-pixels 634 intosub-pixels 636 based on symmetries, if any, that are identified withinthe sub-pixels 634, as it is common for images to possess symmetricalcharacteristics. For example, an image can be left-right symmetric(i.e., across a middle vertical line), top-bottom symmetric (i.e.,around a middle horizontal line), diagonally symmetric (i.e., across amiddle diagonal line), and so forth. According to some embodiments, thesymmetry identifier 508 can be configured to analyze identify whetherthe sub-pixels 634 possess any form of symmetry. For example, when thesub-pixels 634 possess symmetry—e.g., left-right, top-bottom, diagonal,etc.—then only a portion of the sub-pixels 634 need to befurther-processed by the image analyzer 110. For example, when amultiple-channel image 108 is both vertically and horizontallysymmetric, the image analyzer 110 can process only a single quadrant ofthe multiple-channel image 108 to increase efficiency. In anotherexample, when the multiple-channel image 108 is diagonally symmetrical,the image analyzer 110 can process only a single triangular portion ofthe multiple-channel image 108 to increase efficiency. It is noted thatthe symmetry identifier 508 can be configured to process the sub-pixels634 at any level of granularity to identify any level of symmetry. Forexample, the symmetry identifier 508 can analyze the sub-pixels 634 on arow-by-row basis to identify partial symmetry that exists within themulti-channel image 108. Additionally, it is noted that the symmetryidentifier 508 can analyze whether one or more identified symmetries(i.e., the sub-pixels 636) contribute to a compression ratio thatexceeds a compression ratio that would otherwise by achieved if thesymmetry identification is disregarded (i.e., the sub-pixels 634).

Turning now to FIG. 6F, a step 607 can involve the predictor 116identifying, for each row of the sub-pixels 636, a predictive function642 (from among a group of predictive functions 642) that yields themost desirable prediction differentials 640 (i.e., prediction error)within the scope of the row (e.g., as described above in conjunctionwith FIG. 2C). For example, an example scenario is shown FIG. 6F inwhich the second row of the sub-pixels 636 that correspond to the lumavalue Y are processed using each of the Left, Up, Mean, and Custompredictive functions 642 to produce prediction differentials 640.Although not illustrated in FIG. 6F, it will be understood that theexample scenario can also involve processing the second row of thedifferent sub-pixels 636 that correspond to the chroma values CO/CGusing each of the Left, Up, Mean, and Custom predictive functions 642 toproduce prediction differentials 640. In turn, the predictiondifferentials 640 (corresponding to the luma value Y and chroma valuesCO/CG within the second row) can be summed to establish a predictiondifferential total (performed separately for each of the predictivefunctions 642 applied against the second row). For example, asillustrated in FIG. 6F, the column “Differential Total” in the table ofpredictive functions 642 can be populated, for each row of sub-pixels636, with the values of the differential totals for the differentpredictive functions 642 applied against the row so that the mostdesirable predictive function 642 can be identified for the row.

In turn the predictor 116 can identify a most effective predictivefunction 642 for the second row based on the prediction differentialtotals. It is noted that the predictor 116 can implement any form ofarithmetic when calculating the prediction differentials 640/predictiondifferential totals described herein. For example, the predictor 116 canbe configured to sum the absolute value of the prediction differentials640 for the sub-pixels 636 of a given row (for each of the differentpredictive functions 642), and select the predictive function 642 thatyields the smallest prediction differential total. In another example,the predictor 116 can (i) sum the prediction differentials 640 for thesub-pixels 636 of a given row (for each of the different predictivefunctions 642), (ii) take the logarithm of the sums to producelogarithmic values, and (iii) select the predictive function 642 thatyields the smallest logarithmic value. It is noted that the predictivefunctions 642 illustrated in FIG. 6F are merely exemplary, and that thepredictor 116 can be configured to implement any number/type ofpredictive functions 642 without departing from the scope of thisdisclosure.

In any case, when the predictor 116 identifies a predictive function 642that is most appropriate for a given row of sub-pixels 636, thepredictor 116 can store an identifier for the predictive function 642within a predictive function data stream 644, as illustrated at step 608of FIG. 6G. For example, as shown in FIG. 6G, the predictor 116 canidentify the index of the selected predictive function 642 (e.g., inaccordance with the table of predictive functions 642 illustrated inFIG. 6F) for each row of the sub-pixels 636, and sequentially store theindices of the selected predictive functions 642 into the predictivefunction data stream 644. Thus, at the conclusion of step 608 in FIG.6G, two separate data streams have been prepared for the compressor(s)120—the alpha channel data stream 618 and the predictive function datastream 644. At this juncture, the selected predictive functions 642 areapplied against their respective rows of sub-pixels 636 to establish theprediction differentials 640 (e.g., as illustrated in FIG. 6F).Additionally, as shown in FIG. 6F, sign bits can be added (asillustrated by the element 638 in FIG. 6F) to the predictiondifferentials 640 to account for any negative differential valuesestablished by way of the predictive functions 642 (e.g., by subtractingpredicted values from their respective sub-pixels 636). At thisjuncture, additional operations can be performed against the predictiondifferentials 640 to further-enhance the resulting compression ratiosthat can be achieved, which are described below in greater detail inconjunction with FIG. 6H.

As shown in FIG. 6H, a final step 609 can involve the encoder 118separating the prediction differentials 640—specifically, the bits ofeach of prediction differential 640—into a least significant byte 652and a most significant byte 654. For example, as shown in FIG. 6H, theprediction differential 640 for the luma value Y (of the sub-pixel 616(1,1))—which includes (1) a sign bit (S16) established by way of thepredictive functions 642 (applied at step 607 of FIG. 6F), and (2) up tofifteen magnitude bits (M15-M1) (i.e., the prediction differential 640itself)—can be separated into a least significant byte 652-1 and a mostsignificant byte 654-1. In particular, the sign bit (S16) can bepositioned within a least significant bit of the least significant byte652, followed by seven of the least significant magnitude bits (M7-M1).Additionally, eight of the most significant magnitude bits (M15-M8) canbe positioned within the most significant byte 654-1.

Additionally, as shown in FIG. 6H, the prediction differential 640 forthe chroma value CO (of the sub-pixel 616 (1,1))—which includes (1) asign bit (S16) established by way of the predictive functions 642(applied at step 607 of FIG. 6F), and (2) up to fifteen magnitude bits(M15-M1) (i.e., (i) the first sign bit established by the color spacetransformation functions 622 (applied at step 220 of FIG. 6B), and (ii)the prediction differential 640 itself)—can be separated into a leastsignificant byte 652-2 and a most significant byte 654-2. Similarly, theprediction differential 640 for the chroma value CG (of the sub-pixel616 (1,1))—which includes (1) the sign bit (S16) established by way ofthe predictive functions 642 (applied at step 607 of FIG. 6F), and (2)up to fifteen magnitude bits (M15-M1) (i.e., (i) the first sign bitestablished by the color space transformation functions 622 (applied atstep 220 of FIG. 6B), and (ii) the prediction differential 640itself)—can be separated into a least significant byte 652-3 and a mostsignificant byte 654-3.

It is noted that the encoder 118 can perform the foregoing techniquesusing a variety of approaches, e.g., performing in-place modificationsif the prediction differentials 640 (and ordered according to thedistribution illustrated in FIG. 6G), copying the predictiondifferentials 640 into respective data structures for the leastsignificant bytes 652 and the most significant bytes 654 (and orderedaccording to the distribution illustrated in FIG. 6G), and so on. It isalso noted that the distributions illustrated in FIG. 6H and describedherein are exemplary, and that any distribution of the bits of theprediction differentials 640 can be utilized without departing from thescope of this disclosure.

In any case, when the encoder 118 establishes the least significantbytes 652 and the most significant bytes 654 for each of the predictiondifferentials 640, the encoder 118 can group the least significant bytes652 into a least significant byte data stream 656 (e.g., in a left toright (i.e., row-wise)/top down (i.e., column-wise) order). Similarly,the encoder 118 can group the most significant bytes 654 into a mostsignificant bye data stream 658 (e.g., in a left to right (i.e.,row-wise)/top down (i.e., column-wise) order). At this juncture, fourdata streams have been established: the alpha channel data stream 618,the predictive function data stream 644, the least significant byte datastream 656, and the most significant byte data stream 658. In turn, theencoder 118 can provide these data streams into the buffer(s) 119, andinvoke the compressor(s) 120 to compress the buffer(s) 119.Subsequently, the compressor(s) 120 can take action and compress thecontents of the buffer(s) 119 to produce a compressed output. In thismanner, the compressed outputs can be joined together to produce acompressed multiple-channel image 122.

FIGS. 7A-7B illustrate a method 700 for the expanded approach forpre-processing a multiple-channel image 108 for compression, accordingto some embodiments. As shown in FIG. 7A, the method 700 begins at step702, where the image analyzer 110 receives image data for themultiple-channel image 108. As described herein, the image data can becomposed of a plurality of pixels 614, where each pixel 614 of theplurality of pixels 614 is composed of sub-pixels 616 that include: ared sub-pixel 616, a green sub-pixel 616, a blue sub-pixel 616, and analpha sub-pixel 616 (e.g., as described above in conjunction with FIG.6A).

At step 704, the image analyzer 110 separates the alpha sub-pixels 616into a first data stream (e.g., the alpha channel data stream 618described above in conjunction with FIG. 6A). At step 706, the imageanalyzer 110 applies, for each pixel 614 of the plurality of pixels 614,invertible transformations (e.g., the transformation functions 622described above in conjunction with FIG. 6B) to the remaining sub-pixels616 of the pixel 614 to produce transformed sub-pixels (e.g., thesub-pixels 624 described above in conjunction with FIG. 6B).

At step 708, the image analyzer 110 applies, for each pixel 614 of theplurality of pixels 614, invertible spatial transformations (e.g., thetransformation functions 626 described above in conjunction with FIG.6C) to the remaining sub-pixels 616 of the pixel 614 to produce updatedsub-pixels (e.g., the sub-pixels 628 described above in conjunction withFIG. 6C).

At step 710, the image analyzer 110, for each pixel 614 of the pluralityof pixels 614, quantizes the remaining sub-pixels 616 of the pixel 614(e.g., the quantization adjustments 630 described above in conjunctionwith FIG. 6D) to produce updated sub-pixels (e.g., the sub-pixels 632described above in conjunction with FIG. 6D).

At step 712, the image analyzer 110 applies, for each pixel 614 of theplurality of pixels 614, an invertible color palette transformation tothe remaining sub-pixels 616 of the pixel 614 (e.g., step 605 describedabove in conjunction with FIG. 6E) to produce updated sub-pixels (e.g.,the sub-pixels 634 described above in conjunction with FIG. 6E).

At step 714, the image analyzer 110 applies, for each pixel 614 of theplurality of pixels 614, an invertible symmetry transformation to theremaining sub-pixels 616 of the pixel 614 (e.g., step 606 describedabove in conjunction with FIG. 6E) to produce updated sub-pixels (e.g.,the sub-pixels 636 described above in conjunction with FIG. 6E).

Turning now to FIG. 7B, at step 716, the image analyzer 110 establishesa second data stream (e.g., the predictive functions data stream 646described above in conjunction with FIG. 6G). At step 718, the imageanalyzer 110 performs the following for each row of pixels 614 in theplurality of pixels 614: (i) identifying a predictive function (e.g., apredictive function 642, as described above in conjunction with FIG. 6F)that yields a most desirable prediction differential total for the rowof pixels 614 (e.g., as described above in conjunction with FIG. 6F),(ii) providing an identifier of the predictive function 642 to thesecond data stream (e.g., as described above in conjunction with FIG.6G), and (iii) converting the sub-pixels 636 of the pixels in the row ofpixels 614 into prediction differentials 640 based on the predictivefunction 642 (e.g., as described above in conjunction with FIGS. 6F-6G).

At step 720, the image analyzer 110 establishes a third data stream anda fourth data stream (e.g., the least significant byte data stream 656and the most significant byte data stream 658, as described above inconjunction with FIG. 6H). At step 722, the image analyzer 110 performsthe following for each pixel 614 of the plurality of pixels 614: (i)encoding the prediction differentials 640 of the pixel 614 into a firstbyte and a second byte (e.g., as described above in conjunction withFIG. 6H), and (ii) providing the first byte and second bytes to thethird and fourth data streams, respectively (e.g., as described above inconjunction with FIG. 6H). Finally, at step 724, the image analyzer 110compresses the first, second, third, and fourth data streams (e.g., asdescribed above in conjunction with FIG. 6H) using the compressor(s)120.

The various aspects, embodiments, implementations or features of thedescribed embodiments can be used separately or in any combination.Various aspects of the described embodiments can be implemented bysoftware, hardware or a combination of hardware and software. Thedescribed embodiments can also be embodied as computer readable code ona computer readable medium. The computer readable medium is any datastorage device that can store data which can thereafter be read by acomputer system. Examples of the computer readable medium includeread-only memory, random-access memory, CD-ROMs, DVDs, magnetic tape,hard disk drives, solid state drives, and optical data storage devices.The computer readable medium can also be distributed overnetwork-coupled computer systems so that the computer readable code isstored and executed in a distributed fashion.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the describedembodiments. However, it will be apparent to one skilled in the art thatthe specific details are not required in order to practice the describedembodiments. Thus, the foregoing descriptions of specific embodimentsare presented for purposes of illustration and description. They are notintended to be exhaustive or to limit the described embodiments to theprecise forms disclosed. It will be apparent to one of ordinary skill inthe art that many modifications and variations are possible in view ofthe above teachings.

What is claimed is:
 1. A method for pre-processing a multiple-channelimage for compression, the method comprising, at a computing device:receiving the multiple-channel image, wherein the multiple-channel imagecomprises a plurality of pixels, and each pixel of the plurality ofpixels is composed of sub-pixels that include at least an alphasub-pixel; separating the alpha sub-pixels into a first data stream; foreach pixel of the plurality of pixels, producing transformed sub-pixelsby applying, to the remaining sub-pixels, two or more transformations;for each row of pixels in the plurality of pixels: converting thetransformed sub-pixels of the pixels in the row into predictiondifferentials based on a predictive function that yields a lowestprediction differential total for the row of pixels, and encoding theprediction differentials of the pixel into a second data stream; andcompressing the first and second data streams.
 2. The method of claim 1,wherein the two or more transformations comprise at least an invertibletransformation, and applying the invertible transformation to thesub-pixels of a pixel of the plurality of pixels produces an equalnumber of transformed sub-pixels.
 3. The method of claim 1, wherein thesub-pixels of each pixel of the plurality of pixels further include: redsub-pixel, a green sub-pixel, and a blue sub-pixel.
 4. The method ofclaim 3, wherein: applying a first invertible transformation to thegreen sub-pixel comprises: subtracting the blue sub-pixel from the redsub-pixel to produce a first transformed sub-pixel, and replacing thegreen sub-pixel with the first transformed sub-pixel; and applying asecond invertible transformation to the blue sub-pixel comprises: addingthe blue sub-pixel to half of the first transformed sub-pixel to producea temporary value, and replacing the blue sub-pixel with a secondtransformed sub-pixel that is equal to the temporary value subtractedfrom the green sub-pixel.
 5. The method of claim 4, further comprising:adding a respective sign bit to each of the first and the secondtransformed sub-pixel.
 6. The method of claim 4, wherein applying athird invertible transformation to the red sub-pixel comprises:replacing the red sub-pixel with a third transformed sub-pixel valuethat is equal to the temporary value added to half of the secondtransformed sub-pixel.
 7. The method of claim 1, wherein the two or moretransformations comprise: an invertible color space transformation, aninvertible spatial transformation, a quantization transformation, aninvertible color palette transformation, and an invertible symmetrytransformation.
 8. At least one non-transitory computer readable storagemedium configured to store instructions that, when executed by aprocessor included in a computing device, cause the computing device topre-process a multiple-channel image for compression, by carrying outsteps that include: receiving the multiple-channel image, wherein themultiple-channel image comprises a plurality of pixels, and each pixelof the plurality of pixels is composed of sub-pixels that include atleast an alpha sub-pixel; separating the alpha sub-pixels into a firstdata stream; for each pixel of the plurality of pixels, producingtransformed sub-pixels by applying, to the remaining sub-pixels, two ormore transformations; for each row of pixels in the plurality of pixels:converting the transformed sub-pixels of the pixels in the row intoprediction differentials based on a predictive function that yields alowest prediction differential total for the row of pixels, and encodingthe prediction differentials of the pixel into a second data stream; andcompressing the first and second data streams.
 9. The at least onenon-transitory computer readable storage medium of claim 8, wherein thetwo or more transformations comprise at least an invertibletransformation, and applying the invertible transformation to thesub-pixels of a pixel of the plurality of pixels produces an equalnumber of transformed sub-pixels.
 10. The at least one non-transitorycomputer readable storage medium of claim 8, wherein the sub-pixels ofeach pixel of the plurality of pixels further include: red sub-pixel, agreen sub-pixel, and a blue sub-pixel.
 11. The at least onenon-transitory computer readable storage medium of claim 10, wherein:applying a first invertible transformation to the green sub-pixelcomprises: subtracting the blue sub-pixel from the red sub-pixel toproduce a first transformed sub-pixel, and replacing the green sub-pixelwith the first transformed sub-pixel; and applying a second invertibletransformation to the blue sub-pixel comprises: adding the bluesub-pixel to half of the first transformed sub-pixel to produce atemporary value, and replacing the blue sub-pixel with a secondtransformed sub-pixel that is equal to the temporary value subtractedfrom the green sub-pixel.
 12. The at least one non-transitory computerreadable storage medium of claim 11, wherein the steps further include:adding a respective sign bit to each of the first and the secondtransformed sub-pixel.
 13. The at least one non-transitory computerreadable storage medium of claim 11, wherein applying a third invertibletransformation to the red sub-pixel comprises: replacing the redsub-pixel with a third transformed sub-pixel value that is equal to thetemporary value added to half of the second transformed sub-pixel. 14.The at least one non-transitory computer readable storage medium ofclaim 8, wherein the two or more transformations comprise: an invertiblecolor space transformation, an invertible spatial transformation, aquantization transformation, an invertible color palette transformation,and an invertible symmetry transformation.
 15. A computing deviceconfigured to pre-process a multiple-channel image for compression, thecomputing device comprising: at least one processor; and at least onememory configured to store instructions that, when executed by the atleast one processor, cause the computing device to: receive themultiple-channel image, wherein the multiple-channel image comprises aplurality of pixels, and each pixel of the plurality of pixels iscomposed of sub-pixels that include at least an alpha sub-pixel;separate the alpha sub-pixels into a first data stream; for each pixelof the plurality of pixels, produce transformed sub-pixels by applying,to the remaining sub-pixels, two or more transformations; for each rowof pixels in the plurality of pixels: convert the transformed sub-pixelsof the pixels in the row into prediction differentials based on apredictive function that yields a lowest prediction differential totalfor the row of pixels, and encode the prediction differentials of thepixel into a second data stream; and compress the first and second datastreams.
 16. The computing device of claim 15, wherein the two or moretransformations comprise at least an invertible transformation, andapplying the invertible transformation to the sub-pixels of a pixel ofthe plurality of pixels produces an equal number of transformedsub-pixels.
 17. The computing device of claim 15, wherein the sub-pixelsof each pixel of the plurality of pixels further include: red sub-pixel,a green sub-pixel, and a blue sub-pixel.
 18. The computing device ofclaim 17, wherein: applying a first invertible transformation to thegreen sub-pixel comprises: subtracting the blue sub-pixel from the redsub-pixel to produce a first transformed sub-pixel, and replacing thegreen sub-pixel with the first transformed sub-pixel; and applying asecond invertible transformation to the blue sub-pixel comprises: addingthe blue sub-pixel to half of the first transformed sub-pixel to producea temporary value, and replacing the blue sub-pixel with a secondtransformed sub-pixel that is equal to the temporary value subtractedfrom the green sub-pixel.
 19. The computing device of claim 18, whereinthe at least one processor further causes the computing device to: add arespective sign bit to each of the first and the second transformedsub-pixel.
 20. The computing device of claim 15, wherein the two or moretransformations comprise: an invertible color space transformation, aninvertible spatial transformation, a quantization transformation, aninvertible color palette transformation, and an invertible symmetrytransformation.